{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPHngOhDyfmIq2qY6WlP33H"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Mount Google Drive\n",
        "Tindakan ini buat memicu otorisasi, yang memungkinkan Colab mengakses akun Google Drive-nya.\n",
        "\n",
        "**Lokasi Dataset : \"/PKM_KC 2025/ML MODEL/DATASET\"**"
      ],
      "metadata": {
        "id": "ur5go8dr543d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengimpor library yang diperlukan dari google.colab\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DctIJFjD5oBY",
        "outputId": "211ad7cb-e5be-4abd-f0f3-c0d4372590e7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dataset\n",
        "\n",
        "## Dataset yang Dipakai:\n",
        "\n",
        "*   **/PPG DATASET - Ashmit Cajla**\n",
        "\n"
      ],
      "metadata": {
        "id": "L4-QT-Xd_BAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Set style untuk visualisasi\n",
        "plt.style.use('default')\n",
        "sns.set_palette(\"husl\")"
      ],
      "metadata": {
        "id": "hDcjvh861hi8"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "dataset_file = '/content/drive/MyDrive/PKM_KC 2025/ML MODEL/DATASET/PPG DATASET - Ashmit Cajla/Dataset for People for their Blood Glucose Level with their Superficial body feature readings..xlsx'\n",
        "\n",
        "try:\n",
        "    df = pd.read_excel(dataset_file, header=2)\n",
        "    print(\"✅ File berhasil dimuat!\")\n",
        "    print(f\"Shape awal dataset: {df.shape}\")\n",
        "\n",
        "except FileNotFoundError:\n",
        "    print(\"❌ ERROR: File tidak ditemukan.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrHcsf1k_ZCi",
        "outputId": "b30114a2-2f4e-406d-da0c-267c759a2e69"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ File berhasil dimuat!\n",
            "Shape awal dataset: (16969, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tampilkan Info Awal\n",
        "print(\"\\n=== INFORMASI DATASET AWAL ===\")\n",
        "print(df.info())\n",
        "print(\"\\n=== 5 BARIS PERTAMA ===\")\n",
        "print(df.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3eL0rHR15I0",
        "outputId": "c4d4da7f-7153-49bd-d11b-7bdc96983f03"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== INFORMASI DATASET AWAL ===\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16969 entries, 0 to 16968\n",
            "Data columns (total 11 columns):\n",
            " #   Column                      Non-Null Count  Dtype   \n",
            "---  ------                      --------------  -----   \n",
            " 0   Age                         16969 non-null  int64   \n",
            " 1   Blood Glucose Level(BGL)    16969 non-null  int64   \n",
            " 2   Diastolic Blood Pressure    16969 non-null  int64   \n",
            " 3   Systolic Blood Pressure     16969 non-null  int64   \n",
            " 4   Heart Rate                  16969 non-null  int64   \n",
            " 5   Body Temperature            16969 non-null  float64 \n",
            " 6   SPO2                        16969 non-null  int64   \n",
            " 7   Sweating  (Y/N)             16969 non-null  int64   \n",
            " 8   Shivering (Y/N)             16969 non-null  int64   \n",
            " 9   Diabetic/NonDiabetic (D/N)  16969 non-null  object  \n",
            " 10  Age Range                   4046 non-null   category\n",
            "dtypes: category(1), float64(1), int64(8), object(1)\n",
            "memory usage: 1.3+ MB\n",
            "None\n",
            "\n",
            "=== 5 BARIS PERTAMA ===\n",
            "   Age  Blood Glucose Level(BGL)  Diastolic Blood Pressure  \\\n",
            "0    9                        79                        73   \n",
            "1    9                        80                        73   \n",
            "2    9                        70                        76   \n",
            "3    9                        70                        78   \n",
            "4   66                       100                        96   \n",
            "\n",
            "   Systolic Blood Pressure  Heart Rate  Body Temperature  SPO2  \\\n",
            "0                      118          98         98.300707    99   \n",
            "1                      119         102         98.300707    94   \n",
            "2                      110          81         98.300707    98   \n",
            "3                      115          96         98.300707    96   \n",
            "4                      144          92         97.807052    98   \n",
            "\n",
            "   Sweating  (Y/N)  Shivering (Y/N) Diabetic/NonDiabetic (D/N) Age Range  \n",
            "0                0                0                          N       NaN  \n",
            "1                1                0                          N       NaN  \n",
            "2                1                0                          N       NaN  \n",
            "3                1                0                          N       NaN  \n",
            "4                0                0                          N         5  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop kolom yang tidak diperlukan\n",
        "columns_to_drop = [\"Diabetic/NonDiabetic (D/N)\", \"Shivering (Y/N)\", \"Sweating  (Y/N)\"]\n",
        "df_clean = df.drop(columns=columns_to_drop)\n",
        "\n",
        "print(f\"\\n✅ Kolom yang di-drop: {columns_to_drop}\")\n",
        "print(f\"Shape dataset setelah dropping: {df_clean.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IP-Qaz2172P",
        "outputId": "4624be39-d1f2-4870-8ee8-b1538978fb53"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "✅ Kolom yang di-drop: ['Diabetic/NonDiabetic (D/N)', 'Shivering (Y/N)', 'Sweating  (Y/N)']\n",
            "Shape dataset setelah dropping: (16969, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename kolom untuk kemudahan\n",
        "column_mapping = {\n",
        "    'Blood Glucose Level(BGL)': 'BGL',\n",
        "    'Diastolic Blood Pressure': 'DBP',\n",
        "    'Systolic Blood Pressure': 'SBP',\n",
        "    'Heart Rate': 'HR',\n",
        "    'Body Temperature': 'Body_Temp',\n",
        "    'SPO2': 'SPO2'\n",
        "}\n",
        "df_clean = df_clean.rename(columns=column_mapping)\n",
        "\n",
        "print(\"\\n=== DATASET SETELAH PREPROCESSING ===\")\n",
        "print(df_clean.info())\n",
        "print(\"\\n=== STATISTIK DESKRIPTIF ===\")\n",
        "print(df_clean.describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3NKu1cG2Lrv",
        "outputId": "cbd1a9bf-9548-46d2-b0cd-2200e0fde22e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== DATASET SETELAH PREPROCESSING ===\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16969 entries, 0 to 16968\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Age        16969 non-null  int64  \n",
            " 1   BGL        16969 non-null  int64  \n",
            " 2   DBP        16969 non-null  int64  \n",
            " 3   SBP        16969 non-null  int64  \n",
            " 4   HR         16969 non-null  int64  \n",
            " 5   Body_Temp  16969 non-null  float64\n",
            " 6   SPO2       16969 non-null  int64  \n",
            "dtypes: float64(1), int64(6)\n",
            "memory usage: 928.1 KB\n",
            "None\n",
            "\n",
            "=== STATISTIK DESKRIPTIF ===\n",
            "                Age           BGL           DBP           SBP            HR  \\\n",
            "count  16969.000000  16969.000000  16969.000000  16969.000000  16969.000000   \n",
            "mean      30.988862     95.731864     77.173493    118.187165     91.524191   \n",
            "std       25.585606     42.990652      7.241511      7.700363     10.409780   \n",
            "min        9.000000     50.000000     60.000000     95.000000     78.000000   \n",
            "25%        9.000000     68.000000     71.000000    113.000000     84.000000   \n",
            "50%       14.000000     83.000000     76.000000    119.000000     89.000000   \n",
            "75%       55.000000    108.000000     83.000000    124.000000     95.000000   \n",
            "max       77.000000    250.000000     98.000000    145.000000    130.000000   \n",
            "\n",
            "          Body_Temp          SPO2  \n",
            "count  16969.000000  16969.000000  \n",
            "mean      97.356146     97.382403  \n",
            "std        0.813555      0.848689  \n",
            "min       96.000132     93.000000  \n",
            "25%       96.674466     97.000000  \n",
            "50%       97.326523     98.000000  \n",
            "75%       97.949904     98.000000  \n",
            "max       98.999792     99.000000  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualisasi Data"
      ],
      "metadata": {
        "id": "I46PxsAG2pbF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribusi Target Variable (BGL)"
      ],
      "metadata": {
        "id": "mAYIB7Zh4kXk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 5))\n",
        "\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.hist(df_clean['BGL'], bins=50, alpha=0.7, color='skyblue', edgecolor='black')\n",
        "plt.title('Distribusi Blood Glucose Level')\n",
        "plt.xlabel('BGL (mg/dL)')\n",
        "plt.ylabel('Frekuensi')\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "stats.probplot(df_clean['BGL'], dist=\"norm\", plot=plt)\n",
        "plt.title('Q-Q Plot BGL')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "pttDzYRD2zAD",
        "outputId": "60625fa9-dedf-461c-c1d5-911233c5d3ee"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation Heatmap"
      ],
      "metadata": {
        "id": "uj9tNL7f4pYj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = df_clean.corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0,\n",
        "            square=True, fmt='.3f', cbar_kws={'label': 'Correlation'})\n",
        "plt.title('Heatmap Korelasi Antar Variabel')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print korelasi dengan target variable\n",
        "print(\"\\n=== KORELASI DENGAN TARGET VARIABLE (BGL) ===\")\n",
        "bgl_corr = correlation_matrix['BGL'].drop('BGL').sort_values(key=abs, ascending=False)\n",
        "for feature, corr in bgl_corr.items():\n",
        "    print(f\"{feature:25}: {corr:6.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "id": "2PNPA8lm4cqh",
        "outputId": "762c7091-af6d-4c84-90c3-fb08289f5cc4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== KORELASI DENGAN TARGET VARIABLE (BGL) ===\n",
            "HR                       : -0.343\n",
            "DBP                      :  0.299\n",
            "SBP                      :  0.179\n",
            "SPO2                     :  0.150\n",
            "Body_Temp                :  0.139\n",
            "Age                      :  0.007\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boxplot"
      ],
      "metadata": {
        "id": "EKUYX_CZ4zFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "df_clean.boxplot(figsize=(15, 10))\n",
        "plt.title('Boxplot untuk Deteksi Outliers')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 727
        },
        "id": "UCpI2cV94dLh",
        "outputId": "85fdc9e9-9614-45d9-fdd7-3d7617fef367"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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YawYaxpqBhrFmoGGsGWgYa6ZlWbt27TaNa/KI/lGf+MQnYpdddon58+fHqFGjolevXrF06dJ6Y2pra2P58uXJ86hfccUVcemll+aur169Ovr27RujR4+O8vLyZp0/DVNTUxPTp0+PY489NkpKSvI9HWjRrBdoGGsGGsaagYaxZqBhrBloGGumZdp4xpOtafaI/j//8z/x/vvvR+/evSMiYtiwYbFy5cqYNWtWDBkyJCIiHnnkkairq4uhQ4du8TFKS0ujtLR0s+0lJSW+6Voorw1sO+sFGsaagYaxZqBhrBloGGsGGsaaaVm29bVocERfs2ZNzJ8/P3d94cKF8dJLL0WPHj2iR48eMXHixDj55JOjV69esWDBgvjmN78ZAwcOjDFjxkRExD777BPHHXdcnH/++fHzn/88ampq4sILL4zTTz89+vTp09DpAAAAAABAsyls6B1eeOGFOPDAA+PAAw+MiIhLL700DjzwwPjud78bRUVF8fLLL8dnPvOZ+OQnPxnnnntuDBkyJJ544ol6R5LfeeedUVlZGaNGjYpx48bF8OHD45e//GXTPSsAAAAAAGgCDT4SfcSIEZFlWfL2Bx98cKuP0aNHj5g8eXJDdw0AAAAAADtUg49EBwAAAACA9kJEBwAAAACABBEdAAAAAAASRHQAAAAAAEgQ0QEAAAAAIKE43xMAAACa1qpVq2Ls2LExb9682GuvveKBBx6Ibt265XtaAADQKonoAADQhgwcODAWLFiQu75s2bLo3r17DBgwIObPn5/HmQEAQOvkdC4AANBGbBrQx4wZE//+7/8eY8aMiYiIBQsWxMCBA/M5PQAAaJVEdAAAaANWrVqVC+hVVVVx3333RWVlZdx3331RVVUVEf8I6atWrcrnNAEAoNUR0QEAoA0YP358REQcd9xxsWLFiujZs2ecfPLJ0bNnz1ixYkWMHj263jgAAGDbOCc6AAC0AW+99VZEREyfPj1233333PYVK1bE7rvvHkVFRfXGAQAA28aR6AAA0AbsscceERGxYcOGiIjo1atXXHzxxdGrV6962zeOAwAAto2IDgAAbcAvf/nL3OVnnnkmqqur47bbbovq6up45plntjgOAADYOqdzAQCANmD48OG5y4cddlju8ooVK+pdHz58eCxfvnyHzg0AAFozR6IDAEAb8MEHHzTpOAAA4B9EdAAAaAM6deqUu/z000/nPki0qKgonn766S2OAwAAts7pXAAAoA0oKCjIXf70pz+du7xhw4Z61zcdBwAAbJ0j0QEAoA1Yu3ZtvesFBQVxzDHHbBbNPzoOAAD4eCI6AAC0AWVlZfWuZ1kWDz30UGRZ9rHjAACAjyeiAwBAG1BSUtKk4wAAgH8Q0QEAoA1Ys2bNZtuGDRu2TeMAAIA0ER0AANqATp06bbZt5syZ2zQOAABIE9EBAKANWLduXe7yK6+8EjvttFMUFRXFTjvtFK+88soWxwEAAFsnogMAQBvw4Ycf5i7vv//+UV5eHhdddFGUl5fH/vvvv8VxAADA1hXnewIAAEDjlZaWxvr163PX33zzzbjlllu2OA4AANh2jkQHAIA24LXXXstd/utf/xo9e/aMkpKS6NmzZ/z1r3/d4jgAAGDrHIkOAAA70Nq1a2POnDnN8tiFhYVRV1cXBxxwQJSVlcWwkcfGC08+GgcccEDu9hUrVsSKFSuafN+VlZVRVlbW5I8LAAD5JqIDAMAONGfOnBgyZEiz72ft2rXx+LT7622rq6trtn3PmjUrDjrooGZ5bAAAyCcRHQAAdqDKysqYNWtWs+5j4cKFccYZZ0RNTU2UlJTE7373u+jfv3+z7rOysrJZHx8AAPJFRAcAgB2orKys2Y/YPuigg+K5ee/GiT97Jv701cNicL+dm3V/AADQlvlgUQAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASCjO9wQAAAAgn1atWhVjx46NefPmxV577RUPPPBAdOvWLd/TAgBaCBEdAACAdmvgwIGxYMGC3PVly5ZF9+7dY8CAATF//vw8zgwAaCmczgUAAIB2adOAPmbMmPj3f//3GDNmTERELFiwIAYOHJjP6QEALYSIDgAAQLuzatWqXEB/++234+23347vf//7ucsR/wjpq1atyuc0AYAWQEQHAACg3Rk/fnxERHTo0CH69u0br732WnzwwQfx2muvRd++faNDhw71xgEA7ZeIDgAAQLvz1ltvRUTEhx9+GBERQ4cOjYkTJ8bQoUPrbd84DgBov0R0AAAA2p1evXrlLr/77rtRUVERv/71r6OioiLefffdLY4DANqn4nxPAKAlWrt2bcyZM6dZ97FmXXU8PXtB7LTLC9GlU2mz7isiorKyMsrKypp9PwAArcEHH3yQu9y7d+/c5TfffLPe9U3HAQDtk4gOsAVz5syJIUOG7JB93bBD9hIxa9asOOigg3bQ3gAAWralS5fWu96rV6845ZRT4u67747FixcnxwEA7Y+IDrAFlZWVMWvWrGbdx9x3V8ald82Omz4/KPbu3b1Z9xXxj+cEAMA/VFRUxPLly3PXFy9eHD/96U+3OA4AaN9EdIAtKCsra/ajtgvffD9Kn1gX++x/QAzut3Oz7gsAgPr69euXO33fyy+/HIceemisX78+OnbsGM8991x86lOfyo0DANo3ER0AAIB2Z9GiRbnLG4N5RMT69evrXd90HADQPhXmewIAAACwow0YMKBJxwEAbZeIDgAAQLtz66235i4/+eSTUVRUFBERRUVF8eSTT25xHADQPonoAAAAtDvjx4/PXR4+fHhs2LAhIiI2bNgQw4cP3+I4AKB9EtEBAABod7b1XOfOiQ4AiOgAAAC0O7vsskvu8ksvvRQdO3aMiIiOHTvGSy+9tMVxAED7VJzvCQAAAMCOtm7dutzlwYMH5y6vX7++3vVNxwEA7ZMj0QEAAGh3li9fXu96aWlpnHbaaVFaWvqx4wCA9kdEBwAAoN3p0aNHvevV1dXx+9//Pqqrqz92HADQ/ojoAAAAtDtZljXpOACg7RLRAQAAaHdWrFhR73pJSUl87nOfi5KSko8dBwC0PyI6AAAA7c5HT9NSU1MTf/zjH6OmpuZjxwEA7U9xvicAAAAtycJlVVFVXZvvaTTagveqcr8XF7f+H/s7lxZH/10653satCEbNmzIXZ41a1YcdthhUVNTEyUlJfHMM8/EkCFDNhsHALRPrf+naQAAaCILl1XFyBsfzfc0mtRld8/O9xSazIxvjBDSaTKrVq3KXd4YzCP+cUT6ptc3HQcAtE8iOgAA/NPGI9BvOW1wDKzokufZNE7VuuqY8ujMOH7EsOjcqTTf02mU+UvXxCW/f6lNvEOAlmPXXXeNqqqqbRoHALRvIjoAAHzEwIousf9u3fI9jUapqamJxbtGHNRvp80+KBGImD59euy1114REfHwww/HscceG3V1dVFYWBjTp0+PUaNG5cYBAO2bDxYFAACg3TnnnHNyl0eNGhV1dXUREVFXV5cL6B8dBwC0TyI6AAAA7c5bb73VpOMAgLZLRAcAAKDd6dmzZ+7y7Nmzo0uXLlFQUBBdunSJ2bNnb3EcANA+OSc6AAAA7c7SpUtzlwcNGpS7vGbNmnrXNx0HALRPjkQHAACg3Xn//ffrXd9zzz3j0ksvjT333PNjxwEA7Y8j0QEAAGh3dt5556iqqspdf+ONN+Kmm27a4jgAoH1zJDoAAADtzogRI5p0HADQdonoAAAAtDuLFy9u0nEAQNslogMAANDu7LXXXk06DgBou0R0AAAA2p3bbrutSccBAG2XiA4AAAAAAAkiOgAAAAAAJIjoAAAAAACQUJzvCQAAQEtSULw6Fq6eG4Udu+R7Ko1SW1sbi2oXxevLX4/i4tb9Y//C1WuioHh1vqcBAEA71bp/mgYAgCZW0v3ZuPK5H+R7Gk3m9qm353sKTaKk+6iIGJfvaQAA0A6J6AAAsImalUPjR+PPjAEVrf9I9KeefCoOH354qz8SfcHSNXHxnQvyPQ0AANqp1v3TNAAANLGstjz6l+8d++7cLd9TaZSamppYWLww9umxT5SUlOR7Oo1St35VZLXv5XsaAAC0Uz5YFAAAAAAAEkR0AAAAAABIENEBAAAAACBBRAcAAAAAgAQRHQAAAAAAEkR0AAAAAABIENEBAAAAACBBRAcAAAAAgAQRHQAAAAAAEkR0AAAAAABIENEBAAAAACBBRAcAAAAAgAQRHQAAAAAAEkR0AAAAAABIENEBAAAAACBBRAcAAAAAgITifE8AAABainU1GyIi4pV3VuV5Jo1Xta46XngvotebK6Jzp9J8T6dR5i9dk+8pAADQjonoAADwTwv+GWu/fc/sPM+kqRTHf89/Pt+TaDKdS/3zBQCAHc9PoQAA8E+j9+sVEREDKrpEp5KiPM+mcea+uyouu3t2/OiUQbF37275nk6jdS4tjv67dM73NAAAaIdEdAAA+KcenTvE6Yfuke9pNIna2tqIiBiwa+fYf7fWH9GhOY0ePTqmTJkS999/f4wbNy6OP/74mDZtWr6nBQC0ECI6AAAA7dq0adOiQ4cO+Z4GANBCFeZ7AgAAAAAA0FI5Eh0AAIAWa+3atTFnzpwmf9ydd9453n///W0a95e//KVJ911ZWRllZWVN+pgAQPMR0QEAAGix5syZE0OGDMnb/t9///0m3/+sWbPioIMOatLHBACaj4gOAABAi1VZWRmzZs1qtsf/uEDeXPutrKxslscFAJqHiA4AAECLVVZW1qxHbWdZFgMHDowFCxbktg0YMCDmz5/fbPsEAFoXHywKAABAuzZ//vx48Y1l0e9bU+LFN5YJ6ABAPSI6AAAAAAAkiOgAAAAAAJAgogMAAAAAQIKIDgAAAAAACSI6AAAAAAAkFOd7AgANtXBZVVRV1+Z7Go224L2q3O/FxW3jj+POpcXRf5fO+Z4GAAAAQJNpG9UGaDcWLquKkTc+mu9pNKnL7p6d7yk0qRnfGCGkAwAAAG2GiA60KhuPQL/ltMExsKJLnmfTOFXrqmPKozPj+BHDonOn0nxPp9HmL10Tl/z+pTbxLgEAAACAjUR0oFUaWNEl9t+tW76n0Sg1NTWxeNeIg/rtFCUlJfmeDgAAAABb4INFAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABIaHNEff/zxOOGEE6JPnz5RUFAQf/rTn+rdnmVZfPe7343evXtHp06d4phjjol58+bVG7N8+fI466yzory8PLp37x7nnnturFmzplFPBAAAAAAAmlqDI3pVVVUccMABcdttt23x9htuuCF+/OMfx89//vN49tlno3PnzjFmzJhYv359bsxZZ50Vr776akyfPj2mTJkSjz/+eHzlK1/Z/mcBAAAAAADNoLihdxg7dmyMHTt2i7dlWRa33HJLfOc734nPfvazERHxf/7P/4mePXvGn/70pzj99NPj9ddfj6lTp8bzzz8fBx98cERE/OQnP4lx48bFjTfeGH369GnE0wEAAAAAgKbT4Ij+cRYuXBiLFy+OY445JretW7duMXTo0Jg5c2acfvrpMXPmzOjevXsuoEdEHHPMMVFYWBjPPvtsfO5zn9vscaurq6O6ujp3ffXq1RERUVNTEzU1NU35FGikja+H14XmUltbm/u9tX+ftbX10pZeG1qmtrZmoLn5cxkaxpqBhvGzGTSMNdMybevr0aQRffHixRER0bNnz3rbe/bsmbtt8eLFUVFRUX8SxcXRo0eP3JiPuv7662PixImbbZ82bVqUlZU1xdRpYtOnT8/3FGij3l4TEVEcTz75ZLzZJd+zaRptZb20xdeGlqmtrBlobhv/XH7mmWfinVfyPRto+awZ2D5+NoOGsWZalrVr127TuCaN6M3liiuuiEsvvTR3ffXq1dG3b98YPXp0lJeX53FmfFRNTU1Mnz49jj322CgpKcn3dGiDXl20Om6c/UwMHz489uvTutd/W1svbem1oWVqa2sGmttf31oeMfuFOOyww+KAPXrkezrQ4lkz0DB+NoOGsWZapo1nPNmaJo3ovXr1ioiIJUuWRO/evXPblyxZEoMHD86NWbp0ab371dbWxvLly3P3/6jS0tIoLS3dbHtJSYlvuhbKa0NzKS4uzv3eVr7H2sp6aYuvDS1TW1kz0Nz8uQwNY83A9vGzGTSMNdOybOtr0aQRvX///tGrV694+OGHc9F89erV8eyzz8ZXv/rViIgYNmxYrFy5MmbNmhVDhgyJiIhHHnkk6urqYujQoU05HQAAAIB2Ze3atTFnzpxm38+addXx9OwFsdMuL0SXTpsf+NiUKisrnc6XZmPNsC0aHNHXrFkT8+fPz11fuHBhvPTSS9GjR4/YY4894pJLLonrrrsu9tprr+jfv3/827/9W/Tp0ydOPPHEiIjYZ5994rjjjovzzz8/fv7zn0dNTU1ceOGFcfrpp0efPn2a7IkBAAAAtDdz5szJHbS4I9ywA/Yxa9asOOigg3bAnmiPrBm2RYMj+gsvvBAjR47MXd94rvKzzz47Jk2aFN/85jejqqoqvvKVr8TKlStj+PDhMXXq1OjYsWPuPnfeeWdceOGFMWrUqCgsLIyTTz45fvzjHzfB0wEAAABovyorK2PWrFnNvp+5766MS++aHTd9flDs3bt7s+6rsrKyWR+f9s2aYVs0OKKPGDEisixL3l5QUBDXXnttXHvttckxPXr0iMmTJzd01wBAC7Uj3gK5I9/+GOEtkABA81i4rCqqqmubdR8deg5o1sePiOhQtypKe62LDj0HRoee3Zp1X39fUROxYlWz7qNzaXH036Vzs+6Dhpv1P2/Gog+WNP+OOjX/LpaVR3Tas1MsK4/o0ql5/wx4581XmvXxIyL6dO0ZQ3bv1+z7aSma9JzoAED7tCPfArkj3v4Y4S2QAEDTW7isKkbe+Gi+p9GkLrt7dr6n0GRmfGOEkN6CLFxWFWf835ujdNeH8z2VJtO5f8Qv/x4Rf8/3TBqv+r1R8eA53283a0ZEBwAabUe8BXJHvv0xwlsgad1eeumlOPDAAyMi4sD/iHjxxRdj8ODB+Z0UAFFVXRsFxavj8rG9o2+P1v2Ot3XVH8YTL8yOIw4eFJ1KO+R7Oo3y9vK18cMH3m32dwjQMFXVtVGzcmhcMuzEVr9eItrgmpnXvtaMiA4ANFpZWVmzH7Vd+Ob7UfrEuthn/wNicL+dm3Vf0Jya+/RHW3pXyMag3pz/2eUUSADbpqT7s/Gz+W3kyNqSiEf/mu9JNI2S7qMiYly+p8Em1tVsiKy2PHqUfCL6lzfvKYN2hKp11TE73o9+XT4ZnXfA6Smb04b1ayKrrcr3NHYoER0AAHagHXn6o49qzv06BRLA1q2r2RA1K4fGVw/5TAys6JLv6TRKWzyqlpZlwdI1ERHx7XvazimDIorjv+c/n+9JNJnOpe0nLbefZwoAAC1Ac53+6JVXXomzzz47IiL+8Ic/xC49e8efZ8yM8SOHxbIl78bJJ58cERG/+c1vYv/992/y/TsFEsDWLVi6JrLa8rj1gXURsS7f02kCe8R985v3Az93nPJ2FQRbg9H79YqIiAEVXaJTSVGeZ9N4c99dFZfdPTt+dMqg2Lt36z+yvr19GK8/HQAAYAdqrtMfbTzKvLCwMPr16xcHH3xwRPzjw3hfeOGFKCwsjLq6ujj77LMjy7Im3z8AW7cjouC6dWtj4fy/Nctjb+rNZWvipofmx6XHDIx+uzTvUfX9B34yOnVq3lOGtbcg2Br06NwhTj90j2bfT3Ofai8i4vDDD4/169dHRMQp/xHRsWPHeOqpp5ptf06z1/REdAAAaEPq6upyAX2jj14HID92RBT8y18WxGljRzTrPjb1zd80/z5mzZoV+w90yjCaRz5Otbd+/Xqn2WtlRHQAAGiDCgoKYsyYMfHggw868hygHWmu04Z91Jp11bnThnVp5g9JdMowmlNzrpltCeXNsW9rpumJ6AAA0AZMmjQpzjnnnIiIePjhh+OII46I+++/P/6//+//iyeeeCJGjRqVGwdA29Vcpw37qJqamlixbGkMO/TgKCkpafb9QXNprjXTu3fv3OVhw4bFySefHI888kgcffTR8Yc//CFmzpwZERHjx4+Pd9/1wbYtnYgOAABtwMaAHhExatSoKCwsjGOOOSZOOumkqKurqzdu4weQQlNYuKwqqqpr8z2NRlvwXlXu9+Li1v9PZed3BsivxYsX5y4///zzuWh+//331/t7ZtNxtFyt/ycDAABgM3V1dTFt2rR8T4M2buGyqhh546P5nkaTuuzu2fmeQpOZ8Y0RQjpAC7DzzjvHxIkTo7S0NKqrq+Pqq6+OJUuW5HtaNICIDgAAbUhBQUH85S9/iQMPPDC37cUXX4yDDjrIudFpchuPQL/ltMExsKJLnmfTOFXrqmPKozPj+BHDonMzn9+5uc1fuiYu+f1LbeIdAgBtwZtvvhlPPPFEPPDAAzF27Nh48803o2PHjvmeFg0gogMAQBvwwgsvxMEHHxxZlkV5eXl8+OGHcf/998e4cePi7bffzgX0F154Ic8zpS0aWNEl9t+tW76n0Sg1NTWxeNeIg/rt5PzOADSpTYP5TTfdlMeZsL0K8z0BAACg8YYMGZK7PGDAgCgtLY0f//jHUVpaGgMGDNjiOAAAmseFF17YpOPILxEdAADaiE1P15JlWTzyyCObbQMAoPn179+/SceRXyI6AAC0IVmWbXbKlhdeeEFABwDYgQYNGhQRETvttFP07Nmz3m09e/aMnXbaqd44WjbnRAcAgDZmyJAh9c6J7vzOAAA71rJlyyIiYsWKFdGzZ8+4/fbbo2PHjrF+/fqYOHFirFixot44WjYRHQAAAACgCfXu3TsiIs4666z4/e9/H1/72tdytxUXF8eZZ54ZkydPzo2jZXM6FwAAAACAJnTEEUfEnnvuGatXr44PPvggbrzxxhg3blzceOON8cEHH8QHH3wQ/fv3jyOOOCLfU2UbOBIdAAAAAKAJFRUVxY9+9KM45ZRT4tRTT43LL788dtttt9htt93i1FNPjSlTpsTdd98dRUVF+Z4q20BEBwAAAABoYieddFLcfffdcdlll8WRRx6Z296/f/+4++6746STTsrj7GgIER0AAAAAoBmcdNJJ8dnPfjZmzJgRDzzwQIwdOzZGjhzpCPRWRkQHAAAAAGgmRUVFcdRRR0VVVVUcddRRAnor5INFAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgoTjfEwAAAKD1KiheHQtXz43Cjl3yPZVGqa2tjUW1i+L15a9HcXHr/qfywtVroqB4db6nAQBtRuv+yQAAAIC8Kun+bFz53A/yPY0mc/vU2/M9hSZR0n1URIzL9zQAoE0Q0QEAANhuNSuHxo/GnxkDKlr/kehPPflUHD788FZ/JPqCpWvi4jsX5HsaANBmtO6fDAAAAMirrLY8+pfvHfvu3C3fU2mUmpqaWFi8MPbpsU+UlJTkezqNUrd+VWS17+V7GgDQZvhgUQAAAAAASBDRAQAAAAAgQUQHAAAAAIAEER0AAAAAABJEdAAAAAAASBDRAQAAAAAgQUQ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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preproccess Data"
      ],
      "metadata": {
        "id": "CkCah3Ws5MfL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_feature = df_clean.copy()\n",
        "\n",
        "# Define the bins (age ranges) and the corresponding labels\n",
        "bins = [0, 18, 35, 50, 65, 100]\n",
        "labels = [1, 2, 3, 4, 5]\n",
        "\n",
        "df_feature['Age_Category'] = pd.cut(df_feature['Age'],\n",
        "                                  bins=bins,\n",
        "                                  labels=labels)\n",
        "\n",
        "df_feature = df_feature.drop(columns=['Age'])\n",
        "\n",
        "df_feature.head()"
      ],
      "metadata": {
        "colab": {
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        "id": "a_cNyCqy5L9Z",
        "outputId": "b4f2ce36-4f04-48ec-cd3b-b7decffcb6ce"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BGL  DBP  SBP   HR  Body_Temp  SPO2 Age_Category\n",
              "0   79   73  118   98  98.300707    99            1\n",
              "1   80   73  119  102  98.300707    94            1\n",
              "2   70   76  110   81  98.300707    98            1\n",
              "3   70   78  115   96  98.300707    96            1\n",
              "4  100   96  144   92  97.807052    98            5"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_feature",
              "summary": "{\n  \"name\": \"df_feature\",\n  \"rows\": 16969,\n  \"fields\": [\n    {\n      \"column\": \"BGL\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42,\n        \"min\": 50,\n        \"max\": 250,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          86,\n          79,\n          62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 60,\n        \"max\": 98,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          64,\n          63,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SBP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 95,\n        \"max\": 145,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          101,\n          95,\n          109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HR\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 78,\n        \"max\": 130,\n        \"num_unique_values\": 53,\n        \"samples\": [\n          88,\n          124,\n          125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Body_Temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8135547195323951,\n        \"min\": 96.00013206839009,\n        \"max\": 98.9997915925016,\n        \"num_unique_values\": 15636,\n        \"samples\": [\n          97.4821122762349,\n          97.03129803503884,\n          97.7725008728993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SPO2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 93,\n        \"max\": 99,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          99,\n          94,\n          97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_feature.drop(columns=['BGL'])  # Fitur\n",
        "y = df_feature['BGL']  # Target\n",
        "\n",
        "# Fitur numerik yang akan di-scale (tanpa Age_Category)\n",
        "features_to_scale = ['DBP', 'SBP', 'HR', 'Body_Temp', 'SPO2']\n",
        "\n",
        "# Inisialisasi dan terapkan StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_scaled_features = scaler.fit_transform(X[features_to_scale])\n",
        "\n",
        "# Buat DataFrame baru untuk fitur yang sudah discale\n",
        "X_scaled_df = pd.DataFrame(X_scaled_features, columns=features_to_scale, index=X.index)\n",
        "\n",
        "# Gabungkan kembali dengan Age_Category\n",
        "X_final = pd.concat([X[['Age_Category']], X_scaled_df], axis=1)\n",
        "\n",
        "# Cek bentuk akhir\n",
        "print(f\"Shape fitur (X_final): {X_final.shape}\")\n",
        "print(f\"Shape target (y): {y.shape}\")\n",
        "\n",
        "# Optional: Lihat beberapa baris pertama\n",
        "print(X_final.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJbJ3Xf99ZcM",
        "outputId": "2db1b160-c54c-47df-d96e-fa7aad74a5c2"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape fitur (X_final): (16969, 6)\n",
            "Shape target (y): (16969,)\n",
            "  Age_Category       DBP       SBP        HR  Body_Temp      SPO2\n",
            "0            1 -0.576346 -0.024307  0.622107   1.161063  1.906051\n",
            "1            1 -0.576346  0.105561  1.006373   1.161063 -3.985563\n",
            "2            1 -0.162056 -1.063249 -1.011020   1.161063  0.727728\n",
            "3            1  0.114138 -0.413910  0.429975   1.161063 -1.628917\n",
            "4            5  2.599880  3.352257  0.045709   0.554258  0.727728\n"
          ]
        }
      ]
    }
  ]
}